CN108258734B - Robust optimal scheduling method based on wind power interval prediction - Google Patents

Robust optimal scheduling method based on wind power interval prediction Download PDF

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CN108258734B
CN108258734B CN201810195547.XA CN201810195547A CN108258734B CN 108258734 B CN108258734 B CN 108258734B CN 201810195547 A CN201810195547 A CN 201810195547A CN 108258734 B CN108258734 B CN 108258734B
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generating unit
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CN108258734A (en
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孙斌
马覃峰
王寅
刘明顺
朱灵子
陈锐
匡洪辉
翟武悝
郭少青
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Guizhou Power Grid Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/386—
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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    • Y02E10/76—Power conversion electric or electronic aspects

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Abstract

The invention discloses a robust optimal scheduling method based on wind power interval prediction, which comprises the following steps of: step 1: carrying out basic data statistics; step 2: constructing an optimization target; and step 3: setting various constraint items; and 4, step 4: and constructing a model for solving according to the target and the constraint term obtained in the step. The invention provides a robust optimal scheduling method based on wind power interval prediction, which can ensure that a system can fully absorb wind power under the condition of fully meeting the wind power change and improve the operation benefit of the system.

Description

Robust optimal scheduling method based on wind power interval prediction
Technical Field
The invention belongs to the field of electric power system dispatching operation. In particular to a robust optimal scheduling method based on wind power interval prediction.
Background
The wind power is rapidly developed, and the installation proportion is continuously increased, so that the wind power becomes an important challenge for the dispatching and running of a power grid. Particularly in the field of optimal scheduling, a traditional power system is mainly based on thermal power, and because the output controllability and the predictability are high, an optimal scheduling model is often constructed by adopting a deterministic method; with the development of wind power, the low prediction precision of wind power enables traditional deterministic optimal scheduling to be incapable of meeting the operation requirements of a power grid.
In order to solve the problems, an uncertain thought is introduced into the optimal scheduling, and the optimal scheduling becomes an important development direction for the current scheduling operation. Currently, there are three main directions of research based on the above ideas:
1) the core idea of the optimal scheduling method based on the wind power probability prediction curve is that a probabilistic model is constructed based on a distribution probability curve of wind power to obtain a thermal power generation plan under the expectation of the maximum operation benefit of a power grid;
2) the core idea of the optimal scheduling method based on wind power multi-scene prediction is that a deterministic model is built based on wind power multi-scene prediction results, so that a thermal power generating set can meet the requirements of different scenes, and the highest operation benefit is obtained.
The two optimal scheduling methods promote the development of uncertain optimal scheduling. However, there are the following problems:
1) the wind power distribution probability is considered in the optimal scheduling method based on the wind power probability prediction curve, the obtained thermal power plan is only an optimal operation scheme under a certain probability actually, the possibility that thermal power output adjustment cannot meet network safety constraint or operation standby constraint in actual operation possibly exists, and the safe operation of a power grid is endangered in serious cases.
2) The thermal power output plan obtained by the optimal scheduling method based on wind power multi-scene prediction can meet the requirements of wind power multi-scene prediction curves, but in practice, the wind power output may deviate from multiple considered scenes, and the problems of the probabilistic optimal scheduling still exist.
In order to solve the above problem, wind power section prediction has been a development direction of wind power prediction in recent years. As shown in fig. 1, wind power interval prediction means that wind power is predicted from basic information such as weather and topography for a certain period of time in the future, and a range of the interval in which the wind power changes is given. The interval prediction requires that the wind power curve in the actual operation process is in the interval range, and from the actual implementation situation, the single-point wind power prediction accuracy in the current country can reach over 90%, and the interval prediction feasibility is high. The wind power interval prediction content comprises the following steps: a highest probability wind power curve, a wind power interval prediction upper limit and a wind power interval prediction lower limit. The highest probability wind power curve refers to a wind power curve with the highest probability in an interval range, and power grid power flow analysis based on the highest probability wind power curve is called ground state analysis.
Disclosure of Invention
In view of the above, the present invention provides a robust optimal scheduling method based on wind power interval prediction. By introducing a robust scheduling idea, a power generation plan of the thermal power generating unit is reasonably compiled, and the operation benefit of the power system is maximized on the basis of ensuring that both the operation standby of a power grid and the network constraint can be self-adapted to the change of the wind power in a prediction interval.
The purpose of the invention is realized by the following technical scheme:
the robust optimal scheduling method based on wind power interval prediction comprises the following steps:
step 1: carrying out basic data statistics;
step 2: constructing an optimization target;
and step 3: setting various constraint items;
and 4, step 4: and constructing a model for solving according to the target and the constraint term obtained in the step.
Further, in step 3, the constraint terms include a constraint term under a ground state condition and a constraint term under an extreme condition, and the types of the constraint terms include a power balance constraint, a network transmission constraint, a unit output limit constraint and a unit climbing constraint.
Further, in step 1, the basic data to be counted includes: 1) the power grid type data comprise network connection relation and line parameters; 2) the unit type data comprise the output adjusting range, the climbing speed and the minimum start-stop time of the thermal power unit; 3) wind power interval prediction data comprise wind power interval prediction upper and lower limits and a highest probability wind power curve; 4) and load prediction data comprises system load prediction and bus load prediction.
Further, in the step 2, the optimization goal of the robust optimal scheduling considering the wind power interval prediction is that the electricity purchasing cost of the system is the lowest. The above objectives are represented as follows:
Figure BDA0001592980940000021
as shown in formula I, T is the number of the optimized time periods, G is the number of the generator sets, Delta T is the time between the time periods,
Figure BDA0001592980940000022
for the running price of the generator set i during the time period t,
Figure BDA0001592980940000023
and planning power generation for the corresponding thermal power generating unit.
Further, in the step 3, power balance constraint needs to ensure that the output plan of the thermal power generating unit can meet the power supply and demand balance requirement under the highest probability wind power prediction curve; extreme scenes needing to be considered comprise two conditions of wind power operation according to the upper limit of the prediction of the interval of the wind power and operation according to the lower limit of the prediction of the interval of the wind power, and under the two conditions, the standby operation of a system needs to meet the requirement of power supply and demand balance;
the above requirements are expressed as follows:
Figure BDA0001592980940000031
the first formula in the formula II is the balance constraint of power supply and demand under the ground state; the second expression in the expression (2) is that when the wind power operates according to the interval prediction lower limit, the system operates with the negative standby constraint requirement; the third expression in the expression (2) is the constraint requirement of the system operation on the positive standby when the wind power is operated according to the upper limit of the interval prediction;
wherein, Pt W,M、Pt W,X、Pt W,SRespectively obtaining the power of a highest probability wind power prediction curve at the moment t, the power of the upper limit of a wind power interval prediction curve at the moment t and the power of the lower limit of the wind power interval prediction curve at the moment t; pt LPredicting curve power for the load at time t; pt R,S、Pt R,XA positive standby and a negative standby are operated for the system at the time t;
the positive backup and the negative backup of the system operation are respectively the sum of the positive backup and the negative backup of all the thermal power generating units in the power grid, and the positive backup and the negative backup of a single thermal power generating unit not only need to consider the limitation of the maximum technical output and the minimum technical output, but also need to consider the influence of the climbing speed. The system operation positive and negative standby is expressed as follows:
Figure BDA0001592980940000032
as shown in formula (iii), Pi E,max、Pi E,minRespectively the maximum and minimum technical output of the generator set i,
Figure BDA0001592980940000033
the up and down climbing rates are set; then
Figure BDA0001592980940000034
In order to obtain the increased capacity of the generator set at time t from its maximum technical output limit,
Figure BDA0001592980940000035
the minimum of the two for the increased force capability resulting from its ramp rate limitation
Figure BDA0001592980940000036
The generator set i is used as a positive standby; accordingly, the method can be used for solving the problems that,
Figure BDA0001592980940000037
the generator set i is used as a negative standby; sum of positive and negative reserve of all units
Figure BDA0001592980940000038
Namely, the system is used as a positive standby and a negative standby.
Further, in step 3, the network transmission constraint requirement includes: (1) under the ground state, the line tide is within the upper and lower limit ranges; (2) under an extreme scene that wind power predicts the operation of upper and lower limits according to the interval of the wind power, regulating the output of a thermal power unit can ensure that the power flow of a line does not exceed the limit;
the above constraints can be expressed as:
Figure BDA0001592980940000039
the first expression and the second expression in the fourth expression are ground state power flow constraints, wherein the first expression is a ground state power flow equation used for solving the ground state circuit power flow, and the second expression is used for ensuring that the circuit power flow does not exceed the limit; the third and fourth formulas in the formula (4) are respectively constraint terms that the line power flow does not exceed the limit after the thermal power unit is adjusted when the wind power is operated at the upper limit and the lower limit of the interval prediction;
wherein B is the imaginary part of the node admittance matrix; p and theta are a node injection active power column vector and a node voltage phase angle column vector respectively;
Figure BDA0001592980940000041
respectively the highest transmission power and the lowest transmission power of the transmission line l;
Figure BDA0001592980940000042
the power flow of the power transmission line l at the moment t under the curve of the highest probability wind power is obtained;
Figure BDA0001592980940000043
is the power flow distribution factor of the thermal power generating unit i or the node where the wind power plant i is located, GW is the number of the wind power plants,
Figure BDA0001592980940000044
respectively positive power adjustment amount and negative power adjustment amount of the thermal power plant when the wind power is the upper limit and the lower limit of the interval.
Further, in step 3, the constraint requirement of the unit output limit includes: (1) under the ground state, the output of the thermal power generating unit is within the maximum and minimum technical output ranges; (2) in an extreme scene, the thermal power generating unit participates in adjustment, the required adjustment capability does not exceed the adjustment capability limit value, and the extreme scene is constraint; (3) the output adjustment amount of the thermal power generating unit is adjusted when the wind power is limited to change at the upper limit and the lower limit of the interval range by network transmission;
the above constraints can be expressed as:
Figure BDA0001592980940000045
in formula (II) Pi E,max、Pi E,minRespectively the maximum and minimum technical output of the generator set i,
Figure BDA0001592980940000046
the up and down climbing rates are set; then
Figure BDA0001592980940000047
In order to obtain the increased capacity of the generator set at time t from its maximum technical output limit,
Figure BDA0001592980940000048
the minimum of the two for the increased force capability resulting from its ramp rate limitation
Figure BDA0001592980940000049
The generator set i is used as a positive standby; accordingly, the method can be used for solving the problems that,
Figure BDA00015929809400000410
and is used as a negative standby of the generator set i.
Further, in step 3, the unit climbing constraint requirement includes: (1) under the ground state, the output change of the thermal power generating unit is within the climbing capacity range; (2) in an extreme scene, the climbing rate of the thermal power generating unit can meet the requirement of the climbing rate of the thermal power generating unit, the extreme scene to be considered comprises (2.1) the wind power at the last moment is the upper prediction limit of the interval, and the wind power at the next moment is the lower prediction limit of the interval; (2.2) the wind power at the last moment is the lower limit of the interval prediction, and the wind power at the next moment is the upper limit of the interval prediction;
the above constraint can be expressed as:
Figure BDA00015929809400000411
in the sixth formula, the first formula is the ramp rate constraint of the thermal power generating unit under the ground state; the second formula is the climbing constraint of the extreme scene I; the third equation is the climbing constraint of the extreme scene 2.
Wherein, Pi E,Pmax、Pi E,PminThe upper limit and the lower limit of the climbing capability of the thermal power generating unit i are respectively set.
Further, in the step 4, the model is a linear programming problem and is obtained by solving through a commercial software package.
The invention has the beneficial effects that: the invention provides a robust optimal scheduling method based on wind power interval prediction, which can ensure that a system can fully absorb wind power under the condition of fully meeting the wind power change and improve the operation benefit of the system.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof.
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In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings, in which:
FIG. 1 is a wind power interval prediction;
FIG. 2 is a flow chart of a method of the present invention.
Detailed Description
Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the preferred embodiments are illustrative of the invention only and are not limiting upon the scope of the invention.
The robust scheduling means that the compiled power generation plan of the thermal power generating unit can meet the change requirement of wind power within the prediction range of a wind power interval, and when the wind power changes in the actual operation process, the operation standby of a power grid and the section flow meet the operation requirement. The robust optimal scheduling method based on wind power interval prediction comprises the following steps:
step 1: carrying out basic data statistics; in this embodiment, the basic data to be counted includes: 1) the power grid type data comprise network connection relation and line parameters; 2) the unit type data comprise the output adjusting range, the climbing speed and the minimum start-stop time of the thermal power unit; 3) wind power interval prediction data comprise wind power interval prediction upper and lower limits and a highest probability wind power curve; 4) and load prediction data comprises system load prediction and bus load prediction.
Step 2: constructing an optimization target; in this embodiment, the optimization goal of robust optimal scheduling considering wind power interval prediction is that the system electricity purchase cost is the lowest, and the goal is expressed as follows:
Figure BDA0001592980940000051
as shown in formula I, T is the number of the optimized time periods, G is the number of the generator sets, Delta T is the time between the time periods,
Figure BDA0001592980940000052
for the running price of the generator set i during the time period t,
Figure BDA0001592980940000053
and planning power generation for the corresponding thermal power generating unit.
And step 3: setting various constraint items; in this embodiment, the constraint terms include a constraint term under a ground state condition and a constraint term under an extreme condition, and the types of the constraint terms include a power balance constraint, a network transmission constraint, a unit output limit constraint, and a unit climbing constraint.
And 4, step 4: and constructing a model solution according to the target and the constraint term obtained in the step, wherein the model is a linear programming problem and is obtained by solving through a commercial software package, and the commercial software package comprises but is not limited to Cplex.
It should be noted that, in step 3, the constraint conditions of the constraint item types are all specifically limited, and are specifically set forth as follows:
(1) and (3) power balance constraint: the output plan of the thermal power generating unit can meet the requirement of power supply and demand balance under the highest probability wind power prediction curve; extreme scenes needing to be considered comprise two conditions of wind power operation according to the upper limit of the prediction of the interval of the wind power and operation according to the lower limit of the prediction of the interval of the wind power, and under the two conditions, the standby operation of a system needs to meet the requirement of power supply and demand balance;
the above requirements are expressed as follows:
Figure BDA0001592980940000061
the first formula in the formula II is the balance constraint of power supply and demand under the ground state; the second expression in the expression (2) is that when the wind power operates according to the interval prediction lower limit, the system operates with the negative standby constraint requirement; the third expression in the expression (2) is the constraint requirement of the system operation on the positive standby when the wind power is operated according to the upper limit of the interval prediction;
wherein, Pt W,M、Pt W,X、Pt W,SRespectively obtaining the power of a highest probability wind power prediction curve at the moment t, the power of the upper limit of a wind power interval prediction curve at the moment t and the power of the lower limit of the wind power interval prediction curve at the moment t; pt LPredicting curve power for the load at time t; pt R,S、Pt R,XA positive standby and a negative standby are operated for the system at the time t;
the positive backup and the negative backup of the system operation are respectively the sum of the positive backup and the negative backup of all the thermal power generating units in the power grid, and the positive backup and the negative backup of a single thermal power generating unit not only need to consider the limitation of the maximum technical output and the minimum technical output, but also need to consider the influence of the climbing speed. The system operation positive and negative standby is expressed as follows:
Figure BDA0001592980940000062
as shown in formula (iii), Pi E,max、Pi E,minRespectively the maximum and minimum technical output of the generator set i,
Figure BDA0001592980940000063
the up and down climbing rates are set; then
Figure BDA0001592980940000064
In order to obtain the increased capacity of the generator set at time t from its maximum technical output limit,
Figure BDA0001592980940000065
the minimum of the two for the increased force capability resulting from its ramp rate limitation
Figure BDA0001592980940000066
The generator set i is used as a positive standby; accordingly, the method can be used for solving the problems that,
Figure BDA0001592980940000067
the generator set i is used as a negative standby; sum of positive and negative reserve of all units
Figure BDA0001592980940000068
Namely, the system is used as a positive standby and a negative standby.
(2) Network transmission constraints: the requirements include: (a) under the ground state, the line tide is within the upper and lower limit ranges; (b) under an extreme scene that wind power predicts the operation of upper and lower limits according to the interval of the wind power, regulating the output of a thermal power unit can ensure that the power flow of a line does not exceed the limit;
the above constraints can be expressed as:
Figure BDA0001592980940000071
the first expression and the second expression in the fourth expression are ground state power flow constraints, wherein the first expression is a ground state power flow equation used for solving the ground state circuit power flow, and the second expression is used for ensuring that the circuit power flow does not exceed the limit; the third and fourth formulas in the formula (4) are respectively constraint terms that the line power flow does not exceed the limit after the thermal power unit is adjusted when the wind power is operated at the upper limit and the lower limit of the interval prediction;
wherein B is the imaginary part of the node admittance matrix; p and theta are a node injection active power column vector and a node voltage phase angle column vector respectively;
Figure BDA0001592980940000072
respectively the highest transmission power and the lowest transmission power of the transmission line l;
Figure BDA0001592980940000073
the power flow of the power transmission line l at the moment t under the curve of the highest probability wind power is obtained;
Figure BDA0001592980940000074
is the power flow distribution factor of the thermal power generating unit i or the node where the wind power plant i is located, GW is the number of the wind power plants,
Figure BDA0001592980940000075
respectively positive power adjustment amount and negative power adjustment amount of the thermal power plant when the wind power is the upper limit and the lower limit of the interval.
(3) And (3) unit output limit constraint: the requirements include: (a) under the ground state, the output of the thermal power generating unit is within the maximum and minimum technical output ranges; (b) in an extreme scene, the thermal power generating unit participates in adjustment, the required adjustment capability does not exceed the adjustment capability limit value, and the extreme scene is constraint; (c) the output adjustment amount of the thermal power generating unit is adjusted when the wind power is limited to change at the upper limit and the lower limit of the interval range by network transmission;
the above constraints can be expressed as:
Figure BDA0001592980940000076
in formula (II) Pi E,max、Pi E,minRespectively the maximum and minimum technical output of the generator set i,
Figure BDA0001592980940000077
the up and down climbing rates are set; then
Figure BDA0001592980940000078
In order to obtain the increased capacity of the generator set at time t from its maximum technical output limit,
Figure BDA0001592980940000079
the minimum of the two for the increased force capability resulting from its ramp rate limitation
Figure BDA00015929809400000710
The generator set i is used as a positive standby; accordingly, the method can be used for solving the problems that,
Figure BDA00015929809400000711
and is used as a negative standby of the generator set i.
(4) Unit climbing restraint: the requirements include: (a) under the ground state, the output change of the thermal power generating unit is within the climbing capacity range; (b) under extreme scene, thermal power unit climbing speed can satisfy its climbing speed requirement, and the extreme scene that needs to consider includes: (b1) the wind power at the last moment is the upper limit of the interval prediction, and the wind power at the next moment is the lower limit of the interval prediction; (b2) the wind power at the last moment is the lower limit of the interval prediction, and the wind power at the next moment is the upper limit of the interval prediction;
the above constraint can be expressed as:
Figure BDA0001592980940000081
in the sixth formula, the first formula is the ramp rate constraint of the thermal power generating unit under the ground state; the second formula is the climbing constraint of the extreme scene I; the third equation is the climbing constraint of the extreme scene 2.
Wherein, Pi E,Pmax、Pi E,PminThe upper limit and the lower limit of the climbing capability of the thermal power generating unit i are respectively set.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.

Claims (2)

1. The robust optimal scheduling method based on wind power interval prediction is characterized by comprising the following steps: the method comprises the following steps:
step 1: carrying out basic data statistics;
step 2: constructing an optimization target;
the optimization goal of robust optimal scheduling considering wind power interval prediction is that the system electricity purchase cost is lowest, and the goal is expressed as follows:
Figure FDA0003242112680000011
as shown in formula I, T is the number of the optimized time segments, G is the number of the thermal power generating units, Delta T is the time between the time segments,
Figure FDA0003242112680000012
for the running price of the thermal power generating unit i in the time period t,
Figure FDA0003242112680000013
generating planned power for the corresponding thermal power generating unit;
and step 3: setting various constraint items; the constraint items comprise constraint items under a ground state condition and constraint items under an extreme condition, and the types of the constraint items comprise power balance constraint, network transmission constraint, unit output limit constraint and unit climbing constraint;
the power balance constraint needs to ensure that the output plan of the thermal power generating unit can meet the power supply and demand balance requirement under the highest probability wind power prediction curve; extreme scenes needing to be considered comprise two conditions of wind power operation according to the upper limit of the prediction of the interval of the wind power and operation according to the lower limit of the prediction of the interval of the wind power, and under the two conditions, the standby operation of a system needs to meet the requirement of power supply and demand balance;
the above requirements are expressed as follows:
Figure FDA0003242112680000014
the first formula in the formula II is the balance constraint of power supply and demand under the ground state; the second expression is that the wind power is operated according to the upper limit of the interval prediction, and the system is operated according to the positive and standby constraint requirements; the third expression in the second expression is that the wind power is operated according to the lower limit of the interval prediction, and the system is operated with the negative standby constraint requirement;
wherein, Pt W,M、Pt W,S、Pt W,XRespectively obtaining the power of a highest probability wind power prediction curve at the moment t, the power of the upper limit of a wind power interval prediction curve at the moment t and the power of the lower limit of the wind power interval prediction curve at the moment t; pt LPredicting curve power for the load at time t; pt R,S、Pt R,XA positive standby and a negative standby are operated for the system at the time t;
the positive backup and the negative backup of the system operation are respectively the sum of the positive backup and the negative backup of all thermal power generating units in the power grid, the positive backup and the negative backup of a single thermal power generating unit not only need to consider the limitation of the maximum and the minimum technical output, but also need to consider the influence of the climbing rate, and the positive backup and the negative backup of the system operation are expressed as follows:
Figure FDA0003242112680000021
as shown in formula (iii), Pi E,max、Pi E,minRespectively the maximum and minimum technical output of the thermal power generating unit i,
Figure FDA0003242112680000022
the up and down climbing rates are set; then
Figure FDA0003242112680000023
In order to obtain the increased output capacity of the thermal power generating unit at the moment t due to the maximum technical output limit,
Figure FDA0003242112680000024
the minimum of the two for the increased force capability resulting from its ramp rate limitation
Figure FDA0003242112680000025
The power generating unit i is used as a positive standby power; accordingly, the method can be used for solving the problems that,
Figure FDA0003242112680000026
the method comprises the following steps of (1) taking the power generating unit i as a negative standby; sum of positive and negative reserve of all units
Figure FDA0003242112680000027
Namely, the system is used as a positive standby and a negative standby;
network transmission constraint requirements include: (1) under the ground state, the line tide is within the upper and lower limit ranges; (2) under an extreme scene that wind power predicts the operation of upper and lower limits according to the interval of the wind power, regulating the output of a thermal power unit can ensure that the power flow of a line does not exceed the limit;
the above constraints can be expressed as:
Figure FDA0003242112680000028
the first expression and the second expression in the fourth expression are ground state power flow constraints, wherein the first expression is a ground state power flow equation used for solving the ground state circuit power flow, and the second expression is used for ensuring that the circuit power flow does not exceed the limit; the third and fourth formulas in the fourth formula are respectively constraint terms that the line tide does not exceed the limit after the thermal power unit is adjusted when the wind power is operated at the upper limit and the lower limit of the interval prediction;
wherein B is the imaginary part of the node admittance matrix; p and theta are a node injection active power column vector and a node voltage phase angle column vector respectively;
Figure FDA0003242112680000029
respectively the highest transmission power and the lowest transmission power of the transmission line l;
Figure FDA00032421126800000210
the power flow of the power transmission line l at the moment t under the curve of the highest probability wind power is obtained;
Figure FDA00032421126800000211
is the power flow distribution factor of the node where the thermal power generating unit i is located,
Figure FDA00032421126800000212
is the power flow distribution factor of the node where the wind farm f is located, GW is the number of the wind farms,
Figure FDA00032421126800000213
respectively positive power adjustment quantity and negative power adjustment quantity of the thermal power plant when the wind power is the upper limit and the lower limit of the interval;
the unit output limit constraint requirements include: (1) under the ground state, the output of the thermal power generating unit is within the maximum and minimum technical output ranges; (2) in an extreme scene, the thermal power generating unit participates in adjustment, the required adjustment capability does not exceed the adjustment capability limit value, and the extreme scene is constraint;
the above constraints can be expressed as:
Figure FDA00032421126800000214
in the formula, Pi E,max、Pi E,minRespectively the maximum and minimum technical output of the thermal power generating unit i,
Figure FDA0003242112680000031
the up and down climbing rates are set; then
Figure FDA0003242112680000032
In order to obtain the increased output capacity of the thermal power generating unit at the moment t due to the maximum technical output limit,
Figure FDA0003242112680000033
the minimum of the two for the increased force capability resulting from its ramp rate limitation
Figure FDA0003242112680000034
The power generating unit i is used as a positive standby power; accordingly, the method can be used for solving the problems that,
Figure FDA0003242112680000035
the method comprises the following steps of (1) taking the power generating unit i as a negative standby;
the unit climbing constraint requirements include: (1) under the ground state, the output change of the thermal power generating unit is within the climbing capacity range; (2) in an extreme scene, the climbing rate of the thermal power generating unit can meet the requirement of the climbing rate of the thermal power generating unit, the extreme scene to be considered comprises (2.1) the wind power at the last moment is the upper prediction limit of the interval, and the wind power at the next moment is the lower prediction limit of the interval; (2.2) the wind power at the last moment is the lower limit of the interval prediction, and the wind power at the next moment is the upper limit of the interval prediction;
the above constraint can be expressed as:
Figure FDA0003242112680000036
in the sixth formula, the first formula is the ramp rate constraint of the thermal power generating unit under the ground state; the second equation is the hill climbing constraint of the extreme scenario (2.1); the third equation is the climbing constraint of the extreme scene (2.2);
wherein, Pi E,Pmax、Pi E,PminRespectively representing the upper limit and the lower limit of the climbing capability of the thermal power generating unit i;
and 4, step 4: and constructing a model for solving according to the target and the constraint term obtained in the step.
2. The robust optimal scheduling method based on wind power interval prediction according to claim 1, wherein: in step 1, the basic data to be counted includes: 1) the power grid type data comprise network connection relation and line parameters; 2) the unit type data comprise the output adjusting range, the climbing speed and the minimum start-stop time of the thermal power unit; 3) wind power interval prediction data comprise wind power interval prediction upper and lower limits and a highest probability wind power curve; 4) and load prediction data comprises system load prediction and bus load prediction.
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